def test(self):
X_lower = np.array([0])
X_upper = np.array([1])
X = init_random_uniform(X_lower, X_upper, 10)
Y = np.sin(X)
kernel = george.kernels.Matern52Kernel(np.ones([1]),
ndim=1)
prior = TophatPrior(-2, 2)
model = GaussianProcess(kernel, prior=prior)
model.train(X, Y)
x_test = init_random_uniform(X_lower, X_upper, 3)
# Shape matching predict
m, v = model.predict(x_test)
assert len(m.shape) == 2
assert m.shape[0] == x_test.shape[0]
assert m.shape[1] == 1
assert len(v.shape) == 2
assert v.shape[0] == x_test.shape[0]
assert v.shape[1] == x_test.shape[0]
#TODO: check gradients
# Shape matching function sampling
x_ = np.linspace(X_lower, X_upper, 10)
x_ = x_[:, np.newaxis]
funcs = model.sample_functions(x_, n_funcs=2)
assert len(funcs.shape) == 2
assert funcs.shape[0] == 2
assert funcs.shape[1] == x_.shape[0]
# Shape matching predict variance
x_test1 = np.array([np.random.rand(1)])
x_test2 = np.random.rand(10)[:, np.newaxis]
var = model.predict_variance(x_test1, x_test2)
assert len(var.shape) == 2
assert var.shape[0] == x_test2.shape[0]
assert var.shape[1] == 1
# Check compatibility with all acquisition functions
acq_func = EI(model,
X_upper=X_upper,
X_lower=X_lower)
acq_func.update(model)
acq_func(x_test)
acq_func = PI(model,
X_upper=X_upper,
X_lower=X_lower)
acq_func.update(model)
acq_func(x_test)
acq_func = LCB(model,
X_upper=X_upper,
X_lower=X_lower)
acq_func.update(model)
acq_func(x_test)
acq_func = InformationGain(model,
X_upper=X_upper,
X_lower=X_lower)
acq_func.update(model)
acq_func(x_test)
# Check compatibility with all incumbent estimation methods
rec = BestObservation(model, X_lower, X_upper)
inc, inc_val = rec.estimate_incumbent(None)
assert len(inc.shape) == 2
assert inc.shape[0] == 1
assert inc.shape[1] == X_upper.shape[0]
assert len(inc_val.shape) == 2
assert inc_val.shape[0] == 1
assert inc_val.shape[1] == 1
rec = PosteriorMeanOptimization(model, X_lower, X_upper)
startpoints = init_random_uniform(X_lower, X_upper, 4)
inc, inc_val = rec.estimate_incumbent(startpoints)
assert len(inc.shape) == 2
assert inc.shape[0] == 1
assert inc.shape[1] == X_upper.shape[0]
assert len(inc_val.shape) == 2
assert inc_val.shape[0] == 1
assert inc_val.shape[1] == 1
rec = PosteriorMeanAndStdOptimization(model, X_lower, X_upper)
startpoints = init_random_uniform(X_lower, X_upper, 4)
inc, inc_val = rec.estimate_incumbent(startpoints)
assert len(inc.shape) == 2
assert inc.shape[0] == 1
assert inc.shape[1] == X_upper.shape[0]
assert len(inc_val.shape) == 2
assert inc_val.shape[0] == 1
assert inc_val.shape[1] == 1
def test(self):
X_lower = np.array([0])
X_upper = np.array([1])
X = init_random_uniform(X_lower, X_upper, 10)
Y = np.sin(X)
kernel = george.kernels.Matern52Kernel(np.ones([1]), ndim=1)
prior = TophatPrior(-2, 2)
model = GaussianProcess(kernel, prior=prior)
model.train(X, Y)
x_test = init_random_uniform(X_lower, X_upper, 3)
# Shape matching predict
m, v = model.predict(x_test)
assert len(m.shape) == 2
assert m.shape[0] == x_test.shape[0]
assert m.shape[1] == 1
assert len(v.shape) == 2
assert v.shape[0] == x_test.shape[0]
assert v.shape[1] == x_test.shape[0]
#TODO: check gradients
# Shape matching function sampling
x_ = np.linspace(X_lower, X_upper, 10)
x_ = x_[:, np.newaxis]
funcs = model.sample_functions(x_, n_funcs=2)
assert len(funcs.shape) == 2
assert funcs.shape[0] == 2
assert funcs.shape[1] == x_.shape[0]
# Shape matching predict variance
x_test1 = np.array([np.random.rand(1)])
x_test2 = np.random.rand(10)[:, np.newaxis]
var = model.predict_variance(x_test1, x_test2)
assert len(var.shape) == 2
assert var.shape[0] == x_test2.shape[0]
assert var.shape[1] == 1
# Check compatibility with all acquisition functions
acq_func = EI(model, X_upper=X_upper, X_lower=X_lower)
acq_func.update(model)
acq_func(x_test)
acq_func = PI(model, X_upper=X_upper, X_lower=X_lower)
acq_func.update(model)
acq_func(x_test)
acq_func = LCB(model, X_upper=X_upper, X_lower=X_lower)
acq_func.update(model)
acq_func(x_test)
acq_func = InformationGain(model, X_upper=X_upper, X_lower=X_lower)
acq_func.update(model)
acq_func(x_test)
# Check compatibility with all incumbent estimation methods
rec = BestObservation(model, X_lower, X_upper)
inc, inc_val = rec.estimate_incumbent(None)
assert len(inc.shape) == 2
assert inc.shape[0] == 1
assert inc.shape[1] == X_upper.shape[0]
assert len(inc_val.shape) == 2
assert inc_val.shape[0] == 1
assert inc_val.shape[1] == 1
rec = PosteriorMeanOptimization(model, X_lower, X_upper)
startpoints = init_random_uniform(X_lower, X_upper, 4)
inc, inc_val = rec.estimate_incumbent(startpoints)
assert len(inc.shape) == 2
assert inc.shape[0] == 1
assert inc.shape[1] == X_upper.shape[0]
assert len(inc_val.shape) == 2
assert inc_val.shape[0] == 1
assert inc_val.shape[1] == 1
rec = PosteriorMeanAndStdOptimization(model, X_lower, X_upper)
startpoints = init_random_uniform(X_lower, X_upper, 4)
inc, inc_val = rec.estimate_incumbent(startpoints)
assert len(inc.shape) == 2
assert inc.shape[0] == 1
assert inc.shape[1] == X_upper.shape[0]
assert len(inc_val.shape) == 2
assert inc_val.shape[0] == 1
assert inc_val.shape[1] == 1
class TestGaussianProcess(unittest.TestCase):
def setUp(self):
self.X = np.random.rand(10, 2)
self.y = np.sinc(self.X * 10 - 5).sum(axis=1)
self.kernel = george.kernels.Matern52Kernel(np.ones(self.X.shape[1]),
ndim=self.X.shape[1])
prior = TophatPrior(-2, 2)
self.model = GaussianProcess(self.kernel,
prior=prior,
normalize_input=False,
normalize_output=False)
self.model.train(self.X, self.y, do_optimize=False)
def test_predict(self):
X_test = np.random.rand(10, 2)
m, v = self.model.predict(X_test)
assert len(m.shape) == 1
assert m.shape[0] == X_test.shape[0]
assert len(v.shape) == 1
assert v.shape[0] == X_test.shape[0]
m, v = self.model.predict(X_test, full_cov=True)
assert len(m.shape) == 1
assert m.shape[0] == X_test.shape[0]
assert len(v.shape) == 2
assert v.shape[0] == X_test.shape[0]
assert v.shape[1] == X_test.shape[0]
K_zz = self.kernel.value(X_test)
K_zx = self.kernel.value(X_test, self.X)
K_nz = self.kernel.value(
self.X) + self.model.noise * np.eye(self.X.shape[0])
inv = spla.inv(K_nz)
K_zz_x = K_zz - np.dot(K_zx, np.inner(inv, K_zx))
assert np.mean((K_zz_x - v)**2) < 10e-5
def test_sample_function(self):
X_test = np.random.rand(8, 2)
n_funcs = 3
funcs = self.model.sample_functions(X_test, n_funcs=n_funcs)
assert len(funcs.shape) == 2
assert funcs.shape[0] == n_funcs
assert funcs.shape[1] == X_test.shape[0]
def test_predict_variance(self):
x_test1 = np.random.rand(1, 2)
x_test2 = np.random.rand(10, 2)
var = self.model.predict_variance(x_test1, x_test2)
assert len(var.shape) == 2
assert var.shape[0] == x_test2.shape[0]
assert var.shape[1] == x_test1.shape[0]
def test_nll(self):
theta = np.array([0.2, 0.2, 0.001])
nll = self.model.nll(theta)
def test_optimize(self):
theta = self.model.optimize()
# Hyperparameters are 2 length scales + noise
assert theta.shape[0] == 3
def test_get_incumbent(self):
inc, inc_val = self.model.get_incumbent()
b = np.argmin(self.y)
np.testing.assert_almost_equal(inc, self.X[b], decimal=5)
assert inc_val == self.y[b]
class TestGaussianProcess(unittest.TestCase):
def setUp(self):
self.X = np.random.rand(10, 2)
self.y = np.sinc(self.X * 10 - 5).sum(axis=1)
self.kernel = george.kernels.Matern52Kernel(np.ones(self.X.shape[1]),
ndim=self.X.shape[1])
prior = TophatPrior(-2, 2)
self.model = GaussianProcess(self.kernel, prior=prior,
normalize_input=False,
normalize_output=False)
self.model.train(self.X, self.y, do_optimize=False)
def test_predict(self):
X_test = np.random.rand(10, 2)
m, v = self.model.predict(X_test)
assert len(m.shape) == 1
assert m.shape[0] == X_test.shape[0]
assert len(v.shape) == 1
assert v.shape[0] == X_test.shape[0]
m, v = self.model.predict(X_test, full_cov=True)
assert len(m.shape) == 1
assert m.shape[0] == X_test.shape[0]
assert len(v.shape) == 2
assert v.shape[0] == X_test.shape[0]
assert v.shape[1] == X_test.shape[0]
K_zz = self.kernel.value(X_test)
K_zx = self.kernel.value(X_test, self.X)
K_nz = self.kernel.value(self.X) + self.model.noise * np.eye(self.X.shape[0])
inv = spla.inv(K_nz)
K_zz_x = K_zz - np.dot(K_zx, np.inner(inv, K_zx))
assert np.mean((K_zz_x - v)**2) < 10e-5
def test_sample_function(self):
X_test = np.random.rand(8, 2)
n_funcs = 3
funcs = self.model.sample_functions(X_test, n_funcs=n_funcs)
assert len(funcs.shape) == 2
assert funcs.shape[0] == n_funcs
assert funcs.shape[1] == X_test.shape[0]
def test_predict_variance(self):
x_test1 = np.random.rand(1, 2)
x_test2 = np.random.rand(10, 2)
var = self.model.predict_variance(x_test1, x_test2)
assert len(var.shape) == 2
assert var.shape[0] == x_test2.shape[0]
assert var.shape[1] == x_test1.shape[0]
def test_nll(self):
theta = np.array([0.2, 0.2, 0.001])
nll = self.model.nll(theta)
def test_optimize(self):
theta = self.model.optimize()
# Hyperparameters are 2 length scales + noise
assert theta.shape[0] == 3
def test_get_incumbent(self):
inc, inc_val = self.model.get_incumbent()
b = np.argmin(self.y)
np.testing.assert_almost_equal(inc, self.X[b], decimal=5)
assert inc_val == self.y[b]
